Electronic textiles or E-textiles represent the next generation of fibres, fabrics and articles produced from them. They can be described as textile materials that think for themselves, for example through the incorporation of electronic devices or smart materials. Many intelligent textiles already feature in advanced types of clothing, principally for protection and safety and for added fashion or convenience.

One of the main reasons for the rapid development of intelligent textiles is the important investment make by the military industry. This is because they are used in different project and implimentations such as extreme winter condition jackets or uniforms that change colour so as to improve camouflage effects. Nowadays, the military industry has become aware of the advantage of sharing knowledge with the various industrial sectors, because with joint collaboration far better results can be obtained through team-work.

E-textiles provide ample evidence of the potential and enormous wealth of opportunities still to be realised in the textile industry in the fashion and clothing sector, as well as in the technical textiles sector. Moreover, these developments will be the result of active collaboration between people from a whole variety of backgrounds and disciplines: engineering, science, design, process development, and business and marketing. Our very day-to-day lives will, within the next few years, be significantly regulated by intelligent devices and many of these devices will be in textiles and clothing.

CLASSIFICATION OF ELECTRONIC TEXTILES:

E-textiles are defined as textiles that can sense and react to environmental conditions or stimuli from mechanical, thermal, chemical, electrical or magnetic sources.
According to functional activity smart textiles can be classified in three categories:

• Passive E-Textiles: The first generations of smart textiles, which can only sense the environmental conditions or stimulus, are called Passive Smart Textiles.
• Active E-Textiles: The second generation has both actuators and sensors. The actuators act upon the detected signal either directly or from a central control unit. Active Smart textiles are shape memory, chameleonic, water-resistant and vapour permeable (hydrophilic/non porous), heat storage, thermo regulated, vapour absorbing, heat evolving fabric and electrically heated suits.
• Ultra E-Textiles: Very smart textiles are the third generation of smart textiles, which can sense, react and adopt themselves to environmental conditions or stimuli. A very smart or intelligent textile essentially consists of a unit, which works like the brain, with cognition, reasoning and activating capacities. The production of very smart textiles is now a reality after a successful marriage of traditional textiles and clothing technology with other branches of science like material science, structural mechanics, sensor and actuator technology, advance processing technology, communication, artificial intelligence, biology etc.
New fibre and textile materials, and miniaturized electronic components make the preparation of smart textiles possible, in order to create truly usable smart clothes. These intelligent clothes are worn like ordinary clothing, providing help in various situations according to the designed applications.

THE scaling of device technologies has made possible significant increases in the embedding of computing devices in our surroundings. Embedded microcontrollers have for many years surpassed microprocessors in the number of devices manufactured. The new trend, however, is the networking of these devices and their ubiquity not only in traditional embedded applications such as control systems, but in items of everyday use, such as clothing, and in living environments. A trend deserving particular attention is that in which large numbers of simple, cheap processing elements are embedded in environments. These environments may cover large spatial extents, as is typically the case in networks of sensors, or may be deployed in more localized constructions, as in the case of electronic textiles. These differing spatial distributions also result in different properties of the networks constituted, such as the necessity to use wireless communication in the case of sensor networks and the feasibility of utilizing cheaper wired communications in the case of electronic textiles. Electronic textiles, or e-textiles, are a new emerging inter disciplinary field of research, bringing together specialists in information technology, microsystems, materials, and textiles. The focus of this new area is on developing the enabling technologies and fabrication techniques for the economical manufacture of large-area, flexible, conformable information systems that are expected to have unique applications for both the consumer electronics and aerospace/military industries. They are naturally of particular interest in wearable computing, where they provide lightweight, flexible computing resources that that are easily integrated or shaped into clothing. Due to their unique requirements, e-textiles pose new challenges to hardware designers and system developers, cutting across the systems, device, and technology levels of abstraction.

RELATED WORK

There have been a handful of attempts to design and build prototype computational textiles. In [2], the authors demonstrate attaching off-the-shelf electrical components, such as microcontrollers, surface mount LEDs, piezoelectric transducers, etc., to traditional clothing material, transforming the cloth into a breadboard of sorts. In fabrics which contain conductive strands, these may be used to provide power to the devices as well as to facilitate communication between devices. In [3], the authors extend the work presented in [2], detailing methods by which items such as user interfaces (keypads) and even chip packages may be constructed directly by a textile process. The routing of electrical power and communications through a wearable fabric was addressed in [4]. In [4], the authors provide a detailed account of physical and electrical components for routing electricity through suspenders made of a fabric with embedded conductive strands. The authors also detail the physical construction of a battery holder to be attached to this power grid, as well as a data link layer protocol for interconnecting devices on a Controller Area Network (CAN) bus, also implemented with the strands embedded in the fabric. A complete apparel with embedded computing elements is described in [5]. The authors describe a jacket designed to be worn in the harsh arctic environment, which augments the capabilities of the wearer with a global positioning system (GPS),sensors (accelerometers, conductivity electrode, heart rate monitors, digital thermometers), and heating. All the components obtain power from a central power source and the user interacts with them through a single user interface. The “wearable motherboard” project and implimentation [6] is a substrate that permits the attachment of computation and sensing devices in much the same manner as a conventional PC motherboard. Its proposed use is to monitor vital signs of its wearer and perform processing. Proposed applications include monitoring vital statistics of soldiers in combat. Adaptive techniques such as code migration and remote execution have previously been employed in server and mobile computing systems.

Beamforming in Wired Sensor Network

Beamforming consists of two primary components—source location and signal extraction. It is desirable to detect the location of a signal source and “focus” on this source. The signals from spatially distributed sensors are sent to a central processor, which processes them to determine the location of the signal source and reconstruct a desired signal. Each received sample is filtered and this filtering could indeed be performed at the sensor. Fig. 5 illustrates the organization for a wired network of sensors used to perform beamforming, deployed, for example, on an e-textile.
The beamforming application is easily partitioned to run over an e-textile. The filtering operation on each collected sample can be considered to be independent of other samples, thus it could be performed individually at each sensor node (slave node). The final signal extraction need only be performed at one node (master node). This division of tasks scales well with an increasing number of sensors since the complexity of processing at each sensor node remains the same and the only increase in complexity is in the number of filtered samples collected at the master.
Our example system operates in periods, during each of which all the slaves collect samples, filter them, and send the filtered samples to the master. The duration of the sampling period will differ for different applications of beamforming. In the case of beamforming for speech applications, an overall sampling rate of 8KHz is sufficient. For geophysical phenomenon, a sampling rate of 1KHz is sufficient. For applications such as tracking motion of animals, a sampling rate of 10Hz is sufficient. In the analysis used throughout the rest of the paper, a sampling rate of 10Hz corresponding to a 100 millisecond sampling period is used. Using a larger sampling rate would shift all the results by a constant factor, but would not change the general trends observed and insights provided.

COMMUNICATION ARCHITECTURE AND FAULT MANAGEMENT

Achieving reliable computation in the presence of failures has been an active area of research dating back to the early years of computing [17], [18]. Unlike large networked systems in which failure usually occurs only in communication links or in computational nodes and communication links with low correlation, in the case of e-textiles, nodes and links coexist in close physical proximity and thus witness a high correlation of failures.
An important issue is fault modeling, according to their type (electrical versus mechanical, intermittent, or permanent). Intermittent failures, such as those due to electrical failures, tend to follow a uniform failure probability distribution in which the failure probability remains constant over time. Mechanical failures, on the other hand, can be modeled with an exponential failure probability distribution, where each failure increases the probability of subsequent failures. A special class of permanent faults are those due to battery depletion. They have the advantage of being predictable, given a sufficiently accurate battery model.